RESULTS

Chapter 5 - PITES-ISA in Catalonia: Innovation in Integrated Care Services for Chronic Patients 73

a central issue, particularly when considering enhancing health-risk assessment and patient stratification combining determinants of health from formal care, informal care, biomedical research and population health.

Chapter 5 - PITES-ISA in Catalonia: Innovation in Integrated Care Services for Chronic Patients 74

its extension to lower risk patients, is being deployed as mainstream services of Hospital Clinic of Barcelona (>1,000 patients/year) and will be extended to a general program for prevention of surgical complications currently being implemented at Hospital Clinic.

The setting at Hospital Clinic aims to solve current practical limitations for extensive deployment of the service(s), namely: i) accessibility, ii) behavioural change component; and, iii) financial sustainability; so that the service(s) can become operational as a standard of care intervention. From a technological point of view, Figure  2 depicts the different tools that are envisaged as facilitators both at the medical (right hand-side) and the informal care (left hand-side) domains.

Figure 2. Depiction of main actors and technological support to the peri-surgical intervention

Collaborative management of complex chronic patients (A3) - Proven efficacy of integrated care interventions assessed through randomized controlled trials may not translate into effectiveness at health system level [16]. In this respect, preparation of the workforce and enhanced clinical stratification have been identified as two key limiting factors for successful deployment of integrated care. Both factors are taken into account in a protocol that have been designed [17] to assess adoption of the two target use cases: Community-based management of complex chronic patients, including transitional care and long-term care, and for patients under long-term oxygen therapy. The protocol relies on the hypothesis that implementation of: (i) structured, but flexible service workflows; that is, a collaborative and adaptive case management approach [11] and (ii) enhanced patient health risk assessment and stratification [12], can overcome current limitations of multi-morbidity management.

The protocol aims to assess this hypothesis considering five pivotal aims (Figure 3) for evaluation of the regional deployment of the two target use cases. Assessment will be carried out following a Triple Aim approach [18,19] considering pre-defined outcome variables for: (i) health and well-being, (ii) experience with care, and (iii) costs, and combining empirical questionnaire data collection, information from electronic medical records and registry data. The main study outcome will be twofold:

(i) demonstration of cost-effectiveness of the interventions; and, (ii) identification of factors that modulate success of large scale deployment.

Chapter 5 - PITES-ISA in Catalonia: Innovation in Integrated Care Services for Chronic Patients 75 Figure 3. Five pivotal aims two achieve successful regional adoption of the community-based protocol for collabo-

rative management of complex chronic patients across health-care tiers

Technological support

Adoption of adaptive case management [11,20] to support collaborative work constitutes an emergent approach that facilitates case managers to adapt well-structured service workflows to the continuously evolving needs of the patients. This implies selection and scheduling of specific tasks during case management and ad-hoc collaboration with other professionals across healthcare and social support tiers, which facilitates collaborative decisions triggered by expected and unexpected events.

Therefore, the innovative care services (A1-A3) carried out in the project will be supported by a software platform that will allow the execution of well-structured but adaptable clinical workflows. This platform will be open source and built-up on top of the current health information systems of the different healthcare providers and using existing regional interoperability infrastructures (Figure 4). In order to support both patient collaborative work and self-management, the personal health folder already deployed in the region is currently being adapted for the purposes of the project as a key component of the Catalan Digital Health Framework [21].

Figure 4. Depiction of main components of the Catalan Digital Health Framework

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Enhanced clinical risk stratification and prediction

Current patient-based health risk predictive models are essentially using clinical variables only. However, three categories of covariates have been identified to show potential for inclusion into patient-based health risk predictive models, as displayed in Figure 5: (i) input from enhanced case finding tools; that is, population-health risk predictive models such as the GMA; (ii) individual clinical, physiological and biological information relevant to the medical problem being assessed; and (iii) subject-specific informal care data including lifestyle, adherence profile, socioeconomic status, requirements in terms of social support and environmental factors. It is hypothesized that inclusion of all these covariates influencing patient health may markedly increase the predictive accuracy and facilitate clinical decision-making based on sound estimates of the prognosis of an individual.

The three categories of covariates shall be dynamically captured from different sources, respectively: (i) population-health risk predictive models; (ii) articulated healthcare and biomedical research knowledge (integration of clinical, physiological and biological/molecular information); and, (iii) in-place personal health folders (lifestyle, adherence profile, socioeconomic status, social support and environmental factors). The implementation of this holistic approach generates novel requirements to be adopted by the field. Main identified barriers and opportunities to enable the required potential for Big Data analytics in health applications have been identified and recently reported in [22].

Figure 5. Four categories of covariates that have been identified to show potential for inclusion into patient-based health risk predictive models